Multimodal identification technique using Iris & Palmprint traits with matching score level in various Color Spaces with BTC of bit plane slices

The paper presents the Multimodal biometric techniques with Iris and Palmprint traits. Here the Different Color spaces are considered with score level fusion to get proposed multimodal identification technique using Block Truncation Coding (BTC) with Bit plane Slicing. Use of Color Spaces makes greater impact on iris images, which results in higher GAR. The experimentation done using test bed with 60 pairs of Iris & Palmprint images for 10 persons. Experimentation results indicate that YCgCb color space performs better than all other considered color spaces for proposed multimodal biometric identification technique. The proposed multimodal identification techniques with score level of Iris: Palmprint fusion with 1:3 proportions has given best genuine acceptance rate with BTC.